CN109363660A - Rhythm of the heart method and server based on BP neural network - Google Patents
Rhythm of the heart method and server based on BP neural network Download PDFInfo
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- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
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Abstract
The present invention is suitable for Study On Intelligent Monitoring Techniques field, provide rhythm of the heart method and server based on BP neural network, the described method includes: being primarily based on original training data, original BP network neural model is trained, obtain trained BP network neural model, trained BP network neural model is used to predict the heart rate range value of multiple children, then the heart rate characteristic of the multiple children in target area is obtained, finally the heart rate characteristic of the multiple children in target area is directed into trained BP network neural model, determine the heart rate range of multiple children in target area；Obtain the practical heart rate of the monitoring terminal of either objective children in target area；If the practical heart rate range exceeds heart rate range, warning note is sent to mobile terminal.Due to the heart rate characteristic of available multiple children, the heart rate range of multiple children in target area is determined using BP network neural model, can be realized and the heart rate of the children in some region is monitored, monitoring result is more comprehensively.
Description
Technical field
The invention belongs to Study On Intelligent Monitoring Techniques fields, more particularly to rhythm of the heart method and clothes based on BP neural network
Business device.
Background technique
Heart rate speed is a significant data in human life characteristic parameter, in kindergarten's monitoring system in China.
In order to protect the safety of kindergarten children, it will usually the heart rate data of real-time monitoring Children in Kindergartens, when exception occurs in heart rate
When, warning note is sent to monitoring system.
Currently, the method for traditional detection heart rate is: the heart rate data for the children that will be monitored and one it is fixed
Threshold range is compared, and when the heart rate data monitored exceeds this threshold range, carries out the transmission of warning note.But
The heart rate situation that this mode is only capable of one children of detection, can not heart rate situation to all children in a region into
Row monitoring, monitors not comprehensive enough.
Summary of the invention
In view of this, the embodiment of the invention provides rhythm of the heart method and server based on BP neural network, with solution
The heart rate data of monitor children is compared in the prior art with a fixed threshold range certainly, works as monitoring
When the heart rate data arrived exceeds this threshold range, the transmission of warning note is carried out.But this mode is only capable of one size of detection
Virgin heart rate situation, can not be monitored the heart rate situation of all children in a region, monitor not comprehensive enough ask
Topic.
The first aspect of the embodiment of the present invention provides a kind of rhythm of the heart method based on BP neural network, comprising:
Based on original training data, original BP network neural model is trained, obtains trained BP network mind
Through model, wherein the original training data is the heart rate characteristic of multiple children of multiple regions different periods, the instruction
The BP network neural model perfected is used to predict the heart rate range value of multiple children；
Obtain the heart rate characteristic of the multiple children in target area；
The heart rate characteristic of the multiple children in the target area is directed into the trained BP network neural model
In, determine the heart rate range of multiple children in target area；
If the heart rate range exceeds heart rate range, warning note is sent to mobile terminal.
The second aspect of the embodiment of the present invention provides a kind of heart rate monitoring unit based on BP neural network, comprising:
Model training module is trained original BP network neural model, obtains for being based on original training data
To trained BP network neural model, wherein the original training data is multiple children's of multiple regions different periods
Heart rate characteristic, the trained BP network neural model are used to predict the heart rate range value of multiple children；
Data acquisition module, for obtaining the heart rate characteristic of the multiple children in target area；
Heart rate range determining module, it is described for the heart rate characteristic of the multiple children in the target area to be directed into
In trained BP network neural model, the heart rate range of multiple children in target area is determined；
It is whole to movement to send warning note if exceeding heart rate range for the heart rate range for warning note module
End.
The third aspect of the embodiment of the present invention provides a kind of server, including memory, processor and is stored in institute
State the computer program that can be run in memory and on the processor, which is characterized in that the processor executes the meter
The step of method as described in relation to the first aspect is realized when calculation machine program.
The fourth aspect of the embodiment of the present invention provides a kind of computer readable storage medium, described computer-readable to deposit
Storage media is stored with computer program, and method as described in relation to the first aspect is realized when the computer program is executed by processor
Step.
Existing beneficial effect is the embodiment of the present invention compared with prior art: the embodiment of the present invention is primarily based on original
Training data is trained original BP network neural model, trained BP network neural model is obtained, wherein original
Training data is the heart rate characteristic of multiple children of multiple regions different periods, and trained BP network neural model is used
In the heart rate range value for predicting multiple children, the heart rate characteristic of the multiple children in target area is then obtained, finally by target
The heart rate characteristic of the multiple children in region is directed into trained BP network neural model, is determined multiple in target area
The heart rate range of children；The practical heart rate for obtaining the monitoring terminal of either objective children in target area, if heart rate range exceeds
Heart rate range then sends warning note to mobile terminal.Due to the heart rate characteristic of available multiple children, BP is utilized
Network neural model determines the heart rate range of multiple children in target area, can be realized the heart to the children in some region
Rate is monitored, and monitoring result is more comprehensively.
Detailed description of the invention
To describe the technical solutions in the embodiments of the present invention more clearly, embodiment or the prior art will be retouched below
Attached drawing needed in stating is briefly described, it should be apparent that, the accompanying drawings in the following description is only of the invention one
A little embodiments for those of ordinary skill in the art without any creative labor, can also basis
These attached drawings obtain other attached drawings.
Fig. 1 is the flow diagram for the rhythm of the heart method based on BP neural network that one embodiment of the invention provides；
Fig. 2 be another embodiment of the present invention provides the rhythm of the heart method based on BP neural network flow diagram；
Fig. 3 is the flow diagram for the rhythm of the heart method based on BP neural network that yet another embodiment of the invention provides；
Fig. 4 is the structural block diagram for the heart rate monitoring unit based on BP neural network that one embodiment of the application provides；
Fig. 5 is the schematic diagram for the server that one embodiment of the invention provides.
Specific embodiment
In being described below, for illustration and not for limitation, such as particular system structure, technology etc are proposed
Detail, to understand thoroughly the embodiment of the present invention.However, it will be clear to one skilled in the art that in these no tools
The present invention also may be implemented in the other embodiments of body details.In other situations, omit to well-known system, device,
The detailed description of circuit and method, in case unnecessary details interferes description of the invention.
In order to illustrate technical solutions according to the invention, the following is a description of specific embodiments.
With reference to Fig. 1, Fig. 1 is the process for the rhythm of the heart method based on BP neural network that one embodiment of the invention provides
Schematic diagram, the method can be applied to server end, and details are as follows:
S101: it is based on original training data, original BP network neural model is trained, trained BP is obtained
Network neural model, wherein original training data is the heart rate characteristic of multiple children of multiple regions different periods, training
Good BP network neural model is used to predict the heart rate range value of multiple children.
In the present embodiment, multiple regions can be kindergarten different in a city.Different periods can be child
The different seasons in garden.Heart rate characteristic includes but is not limited to age, mood states and heart rate of children etc..Original BP net
Network neural model is the heart rate range value for assigning different weight coefficients according to heart rate characteristic and multiple children being calculated
Mathematical model.
S102: the heart rate characteristic of the multiple children in target area is obtained.
In the present embodiment, target area can be any one kindergarten of city incity or in which a class.
Here, heart rate characteristic includes but is not limited to age, mood states and heart rate of children etc..
S103: the heart rate characteristic of the multiple children in target area is directed into trained BP network neural model,
Determine the heart rate range of multiple children in target area.
In the present embodiment, age, mood states and the heart rate of children multiple in target area are imported into trained
BP network neural model.Obtain the heart rate range of multiple children in target area.
S104: obtaining the practical heart rate of the monitoring terminal of either objective children in target area, if practical heart rate range is super
Heart rate range out then sends warning note to mobile terminal.
In the present embodiment, heart rate range can be comprehensive according to the age of children, gender and automatic collection heart rate characteristic
It is given to close evaluation.Mobile terminal includes but is not limited to tablet computer (Personal Digital Assistant, PDA) or hand
Machine etc..
Wherein monitoring terminal is Intelligent bracelet, which includes Centralized Controller, heart rate sensor, gain amplification
Device, filter, GPS module, GPRS antenna, power-supply system managing chip, solenoid valve, battery and mini microphone, wherein described
Centralized Controller use the arm7 core MCU of MTK2503, be a highly integrated GPS, BT, PMIC integrate 32M Rom and
The Ram of 32M, heart rate sensor are linked together by I2C bus and MCU controller.The gain amplifier passes through data line
It links together with MCU controller.Filter is linked together by data line and gain amplifier.Outside Centralized Controller
It is provided with GPS module, the outside of module is equipped with GPIO interface, passes through one ceramic antenna of GPIO mouthfuls of connections, so that it may receive
GPS signal, solenoid valve and battery are connected to GPIO interface by PNP high power triode, to pass through the switch control of solenoid valve
The switch of power supply processed realizes the function of remote unlocking.Mini microphone is connected to one by AUDIO interface and MCU controller
It rises, carries out sound-recording function.
As can be seen from the above description, the present embodiment is primarily based on original training data, to original BP network neural model into
Row training, obtains trained BP network neural model, and wherein original training data is multiple youngsters of multiple regions different periods
Virgin heart rate characteristic, trained BP network neural model are used to predict the heart rate range value of multiple children, then obtain
The heart rate characteristic of the multiple children in target area is finally directed into instruction by the heart rate characteristic of the multiple children in target area
In the BP network neural model perfected, the heart rate range of multiple children in target area is determined；Obtain any mesh in target area
The practical heart rate of the monitoring terminal of children is marked, if practical heart rate range exceeds heart rate range, it is whole to movement to send warning note
End.Due to the heart rate characteristic of available multiple children, determined using BP network neural model multiple in target area
The heart rate range of children can be realized and be monitored to the heart rate of the children in some region, and monitoring result is more comprehensively
With reference to Fig. 2, Fig. 2 be another embodiment of the present invention provides the rhythm of the heart method based on BP neural network stream
Journey schematic diagram, on the basis of the above embodiments, step S101 are based on original training data, to original BP network neural mould
Type is trained, and obtains trained BP network neural model, comprising:
S201: original training data is normalized, and is input to original BP network neural model and is instructed
Practice, wherein being constantly adjusted to the weight coefficient in original BP network neural model in the training process.
In the present embodiment, normalized refers to the expression formula that will have dimension, by transformation, turns to nondimensional
Expression formula becomes scalar.
S202: judge whether the error of BP network neural model after adjusting is less than default error threshold.
S203: if error is less than or equal to default error threshold, using BP network neural model adjusted as training
Good BP network neural model.
S204: it if error is greater than default error threshold, continues to execute to the coefficient in original BP network neural model
It is adjusted, and the step of whether error in judgement is less than default error threshold.
In the present embodiment, default error threshold can be configured according to demand.
Compare as can be seen from the above description, being less than or equal to default error threshold by error, determines trained BP network
Neural model improves the accuracy of BP network neural model, to obtain more accurate prediction result.
With reference to Fig. 3, Fig. 3 is the stream for the rhythm of the heart method based on BP neural network that yet another embodiment of the invention provides
Journey schematic diagram, on the basis of the above embodiments, in step s 103 by the heart rate characteristic of the multiple children in the target area
According to being directed into the trained BP network neural model, the heart rate range of multiple children in target area is determined, comprising:
S301: trained BP network neural model is saved as into Net.
In the present embodiment, Net is neural network.
S302: it after the heart rate characteristic of the multiple children in target area is normalized, is loaded into the Net and obtains
To the heart rate range of the multiple children in target area.
In the present embodiment, the heart rate range of children is a value range.
It should be understood that the size of the serial number of each step is not meant that the order of the execution order in above-described embodiment, each process
Execution sequence should be determined by its function and internal logic, the implementation process without coping with the embodiment of the present invention constitutes any limit
It is fixed.
The rhythm of the heart method of BP neural network corresponding to foregoing embodiments, Fig. 4 are what one embodiment of the application provided
The structural block diagram of heart rate monitoring unit based on BP neural network.For ease of description, it illustrates only and the embodiment of the present application phase
The part of pass.With reference to Fig. 4, which includes: model training module 401, data acquisition module 402, heart rate range determining module
403 and warning note module 404.
Model training module 401, for being trained to original BP network neural model based on original training data,
Trained BP network neural model is obtained, wherein the original training data is multiple children of multiple regions different periods
Heart rate characteristic, the trained BP network neural model is used to predict the heart rate range value of multiple children；
Data acquisition module 402, for obtaining the heart rate characteristic of the multiple children in target area；
Heart rate range determining module 403, for the heart rate characteristic of the multiple children in the target area to be directed into institute
It states in trained BP network neural model, determines the heart rate range of multiple children in target area；
Warning note module 404, the practical heart rate of the monitoring terminal for obtaining either objective children in target area,
If the heart rate range exceeds heart rate range, warning note is sent to mobile terminal.
In one embodiment of the invention, the model training module 401, be specifically used for by original training data into
Row normalized is input to original BP network neural model and is trained, wherein in the training process to original BP net
Weight coefficient in network neural model is constantly adjusted；It is pre- to judge whether the error of BP network neural model after adjusting is less than
If error threshold；If the error is less than or equal to default error threshold, using BP network neural model adjusted as instruction
The BP network neural model perfected.
In one embodiment of the invention, the model training module 401 is preset if being also used to the error and being greater than
Error threshold is then continued to execute and is adjusted to the coefficient in original BP network neural model, and whether error in judgement is less than
The step of default error threshold.
In one embodiment of the invention, the heart rate range determining module 403 is specifically used for trained BP
Network neural model saves as Net；After the heart rate characteristic of the multiple children in target area is normalized, it is loaded into
The Net obtains the heart rate range of the multiple children in target area.
Fig. 5 is the schematic diagram for the server that one embodiment of the invention provides.As shown in figure 5, the server 6 of the embodiment
Include: processor 60, memory 61 and is stored in the calculating that can be run in the memory 61 and on the processor 60
Machine program 62, such as the rhythm of the heart program based on BP neural network.When the processor 60 executes the computer program 62
Realize the step in above-mentioned each rhythm of the heart embodiment of the method based on BP neural network, such as step 101 shown in FIG. 1
To 104.Alternatively, the processor 60 realizes each module in above-mentioned each Installation practice/mono- when executing the computer program 62
The function of member, such as the function of module 501 to 504 shown in Fig. 4.
Illustratively, the computer program 62 can be divided into one or more module/units, it is one or
Multiple module/the units of person are stored in the memory 61, and are executed by the processor 60, to complete the present invention.It is described
One or more module/units can be the series of computation machine program instruction section that can complete specific function, which uses
In implementation procedure of the description computer program 62 in the server 6.For example, the computer program 62 can be by
It is divided into model training module 401, data acquisition module 402, heart rate range determining module 403 and warning note module 404.
The server may include, but be not limited only to, processor 60, memory 61.Those skilled in the art can manage
Solution, Fig. 5 is only the example of server 6, does not constitute the restriction to server 6, may include more more or fewer than illustrating
Component perhaps combines certain components or different components, such as the server can also include input-output equipment, net
Network access device, bus etc..
Alleged processor 60 can be central processing unit (Central Processing Unit, CPU), can also be
Other general processors, digital signal processor (Digital Signal Processor, DSP), specific integrated circuit
(Application Specific Integrated Circuit, ASIC), ready-made programmable gate array (Field-
Programmable Gate Array, FPGA) either other programmable logic device, discrete gate or transistor logic device
Part, discrete hardware components etc..General processor can be microprocessor or the processor is also possible to any conventional processing
Device etc..
The memory 61 can be the internal storage unit of the server 6, such as the hard disk or memory of server 6.
The memory 61 is also possible to the External memory equipment of the server 6, such as the plug-in type being equipped on the server 6
Hard disk, intelligent memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card, flash card
(Flash Card) etc..Further, the memory 61 can also both including the server 6 internal storage unit or
Including External memory equipment.The memory 61 is for storing needed for the computer program and the server other
Program and data.The memory 61 can be also used for temporarily storing the data that has exported or will export.
It is apparent to those skilled in the art that for convenience of description and succinctly, only with above-mentioned each function
Can unit, module division progress for example, in practical application, can according to need and by above-mentioned function distribution by difference
Functional unit, module complete, i.e., the internal structure of described device is divided into different functional unit or module, with complete
All or part of function described above.Each functional unit in embodiment, module can integrate in a processing unit
In, it is also possible to each unit and physically exists alone, can also be integrated in one unit with two or more units, on
It states integrated unit both and can take the form of hardware realization, can also realize in the form of software functional units.In addition,
Each functional unit, module specific name be also only for convenience of distinguishing each other, the protection model being not intended to limit this application
It encloses.The specific work process of unit in above system, module, can refer to corresponding processes in the foregoing method embodiment,
This is repeated no more.
In the above-described embodiments, it all emphasizes particularly on different fields to the description of each embodiment, is not described in detail or remembers in some embodiment
The part of load may refer to the associated description of other embodiments.
Those of ordinary skill in the art may be aware that described in conjunction with the examples disclosed in the embodiments of the present disclosure
Unit and algorithm steps can be realized with the combination of electronic hardware or computer software and electronic hardware.These functions
It is implemented in hardware or software actually, the specific application and design constraint depending on technical solution.Professional technique
Personnel can use different methods to achieve the described function each specific application, but this realization should not be recognized
It is beyond the scope of this invention.
In embodiment provided by the present invention, it should be understood that disclosed server and method can pass through it
Its mode is realized.For example, device/server example described above is only schematical, for example, the module
Or the division of unit, only a kind of logical function partition, there may be another division manner in actual implementation, such as multiple
Unit or assembly can be combined or can be integrated into another system, or some features can be ignored or not executed.It is another
Point, shown or discussed mutual coupling or direct-coupling or communication connection can be through some interfaces, device or
The INDIRECT COUPLING or communication connection of unit can be electrical property, mechanical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, as unit
The component of display may or may not be physical unit, it can and it is in one place, or may be distributed over more
In a network unit.Some or all of unit therein can be selected to realize this embodiment scheme according to the actual needs
Purpose.
It, can also be in addition, the functional units in various embodiments of the present invention may be integrated into one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.Above-mentioned integrated list
Member both can take the form of hardware realization, can also realize in the form of software functional units.
If the integrated module/unit is realized in the form of SFU software functional unit and sells as independent product
Or it in use, can store in a computer readable storage medium.Based on this understanding, the present invention realizes above-mentioned
All or part of the process in embodiment method can also instruct relevant hardware to complete by computer program, described
Computer program can be stored in a computer readable storage medium, which, can be real when being executed by processor
The step of existing above-mentioned each embodiment of the method.Wherein, the computer program includes computer program code, the computer
Program code can be source code form, object identification code form, executable file or certain intermediate forms etc..The computer
Readable medium may include: any entity or device, recording medium, USB flash disk, the shifting that can carry the computer program code
Dynamic hard disk, magnetic disk, CD, computer storage, read-only memory (ROM, Read-Only Memory), random access memory
Device (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium etc..It needs to illustrate
, content that the computer-readable medium includes can according to make laws in jurisdiction and the requirement of patent practice into
Row increase and decrease appropriate, such as do not include electricity according to legislation and patent practice, computer-readable medium in certain jurisdictions
Carrier signal and telecommunication signal.
Embodiment described above is merely illustrative of the technical solution of the present invention, rather than its limitations；Although referring to aforementioned reality
Applying example, invention is explained in detail, those skilled in the art should understand that: it still can be to aforementioned each
Technical solution documented by embodiment is modified or equivalent replacement of some of the technical features；And these are modified
Or replacement, the spirit and scope for technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution should all
It is included within protection scope of the present invention.
Claims (10)
1. a kind of rhythm of the heart method based on BP neural network, which is characterized in that be applied to server end, comprising:
Based on original training data, original BP network neural model is trained, trained BP network neural mould is obtained
Type, wherein the original training data is the heart rate characteristic of multiple children of multiple regions different periods, it is described to train
BP network neural model be used to predict the heart rate range value of multiple children；
Obtain the heart rate characteristic of the multiple children in target area；
The heart rate characteristic of the multiple children in the target area is directed into the trained BP network neural model, really
Set the goal the heart rate range of multiple children in region；
The practical heart rate of the monitoring terminal of either objective children in target area is obtained, if the practical heart rate range is beyond described
Heart rate range then sends warning note to mobile terminal.
2. the rhythm of the heart method based on BP neural network as described in claim 1, which is characterized in that described to be based on original instruction
Practice data, original BP network neural model be trained, trained BP network neural model is obtained, comprising:
Original training data is normalized, original BP network neural model is input to and is trained, wherein instructing
The weight coefficient in original BP network neural model is constantly adjusted during white silk；
Judge whether the error of BP network neural model after adjusting is less than default error threshold；
If the error is less than or equal to default error threshold, using BP network neural model adjusted as trained BP
Network neural model.
3. the rhythm of the heart method based on BP neural network as claimed in claim 2, which is characterized in that further include:
If the error is greater than default error threshold, continues to execute and the coefficient in original BP network neural model is adjusted
The step of whole, and whether error in judgement is less than default error threshold.
4. the rhythm of the heart method based on BP neural network as described in claim 1, which is characterized in that described by the target
The heart rate characteristic of the multiple children in region is directed into the trained BP network neural model, is determined more in target area
The heart rate range of a children, comprising:
Trained BP network neural model is saved as into Net；
After the heart rate characteristic of the multiple children in target area is normalized, it is loaded into the Net and obtains target area
The heart rate range of multiple children.
5. such as the described in any item rhythm of the heart methods based on BP neural network of Claims 1-4, which is characterized in that heart rate
Characteristic includes age, mood states and the heart rate of children.
6. a kind of heart rate monitoring unit based on BP neural network, which is characterized in that be applied to server end, comprising:
Model training module is trained original BP network neural model, is trained for being based on original training data
Good BP network neural model, wherein the original training data is the heart rate feature of multiple children of multiple regions different periods
Data, the trained BP network neural model are used to predict the heart rate range value of multiple children；
Data acquisition module, for obtaining the heart rate characteristic of the multiple children in target area；
Heart rate range determining module, for the heart rate characteristic of the multiple children in the target area to be directed into described train
BP network neural model in, determine the heart rate range of multiple children in target area；
Warning note module, the practical heart rate of the monitoring terminal for obtaining either objective children in target area, if the heart
Rate range exceeds heart rate range, then sends warning note to mobile terminal.
7. as claimed in claim 6 based on the heart rate monitoring unit of BP neural network, which is characterized in that the model training mould
Block is input to original BP network neural model and is trained specifically for original training data to be normalized,
In the weight coefficient in original BP network neural model is constantly adjusted in the training process；BP network after judgement adjustment
Whether the error of neural model is less than default error threshold；It, will adjustment if the error is less than or equal to default error threshold
BP network neural model afterwards is as trained BP network neural model.
8. as claimed in claim 6 based on the heart rate monitoring unit of BP neural network, which is characterized in that the heart rate range is true
Cover half block, specifically for trained BP network neural model is saved as Net；By the heart rate feature of the multiple children in target area
After data are normalized, it is loaded into the Net and obtains the heart rate range of the multiple children in target area.
9. a kind of server, including memory, processor and storage can transport in the memory and on the processor
Capable computer program, which is characterized in that the processor realizes such as claim 1 to 5 times when executing the computer program
The step of one the method.
10. a kind of computer readable storage medium, the computer-readable recording medium storage has computer program, and feature exists
In when the computer program is executed by processor the step of any one of such as claim 1 to 5 of realization the method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811258523.0A CN109363660B (en) | 2018-10-26 | 2018-10-26 | Heart rate monitoring method and server based on BP neural network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811258523.0A CN109363660B (en) | 2018-10-26 | 2018-10-26 | Heart rate monitoring method and server based on BP neural network |
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| Publication Number | Publication Date |
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| CN109363660A true CN109363660A (en) | 2019-02-22 |
| CN109363660B CN109363660B (en) | 2022-04-12 |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110163126A (en) * | 2019-05-06 | 2019-08-23 | 北京华捷艾米科技有限公司 | A kind of biopsy method based on face, device and equipment |
| CN110664391A (en) * | 2019-10-15 | 2020-01-10 | 国网福建省电力有限公司检修分公司 | Electric shock emergency safety protection bracelet and working method thereof |
| CN114530028A (en) * | 2022-02-14 | 2022-05-24 | 大连理工大学 | Campus student intelligent bracelet monitoring system and method based on LoRa communication and federal learning |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050058970A1 (en) * | 2003-02-27 | 2005-03-17 | Neil Perlman | Behavior modification aide |
| JP2010503471A (en) * | 2006-09-18 | 2010-02-04 | コーニンクレッカ フィリップス エレクトロニクス エヌ ヴィ | IP-based monitoring and alarming |
| WO2011072111A2 (en) * | 2009-12-09 | 2011-06-16 | Nike International Ltd. | Athletic performance monitoring system utilizing heart rate information |
| US20140330094A1 (en) * | 2000-06-16 | 2014-11-06 | Bodymedia, Inc. | System for monitoring and presenting health, wellness and fitness utilizng multiple user trend data having user selectable parameters |
| CN104720783A (en) * | 2013-12-24 | 2015-06-24 | 中国移动通信集团公司 | Exercise heart rate monitoring method and apparatus |
| CN107157492A (en) * | 2017-05-19 | 2017-09-15 | 国家电网公司 | A kind of embedded human physiologic information non-invasive detection system and data processing method |
-
2018
- 2018-10-26 CN CN201811258523.0A patent/CN109363660B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140330094A1 (en) * | 2000-06-16 | 2014-11-06 | Bodymedia, Inc. | System for monitoring and presenting health, wellness and fitness utilizng multiple user trend data having user selectable parameters |
| US20050058970A1 (en) * | 2003-02-27 | 2005-03-17 | Neil Perlman | Behavior modification aide |
| JP2010503471A (en) * | 2006-09-18 | 2010-02-04 | コーニンクレッカ フィリップス エレクトロニクス エヌ ヴィ | IP-based monitoring and alarming |
| WO2011072111A2 (en) * | 2009-12-09 | 2011-06-16 | Nike International Ltd. | Athletic performance monitoring system utilizing heart rate information |
| CN104720783A (en) * | 2013-12-24 | 2015-06-24 | 中国移动通信集团公司 | Exercise heart rate monitoring method and apparatus |
| CN107157492A (en) * | 2017-05-19 | 2017-09-15 | 国家电网公司 | A kind of embedded human physiologic information non-invasive detection system and data processing method |
Non-Patent Citations (1)
| Title |
|---|
| 周嘉琳 等: "行为地图在幼儿园户外活动环境与幼儿身体活动关系研究中的应用", 《中国体育科技》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110163126A (en) * | 2019-05-06 | 2019-08-23 | 北京华捷艾米科技有限公司 | A kind of biopsy method based on face, device and equipment |
| CN110664391A (en) * | 2019-10-15 | 2020-01-10 | 国网福建省电力有限公司检修分公司 | Electric shock emergency safety protection bracelet and working method thereof |
| CN114530028A (en) * | 2022-02-14 | 2022-05-24 | 大连理工大学 | Campus student intelligent bracelet monitoring system and method based on LoRa communication and federal learning |
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|---|---|
| CN109363660B (en) | 2022-04-12 |
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